Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)

By Linwei Hu, V. Nair, Agus Sudjianto. 2018

66 citations recorded by the source corpus.

Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive performance over traditional statistical methods. This is especially true with large data sets (millions or more observations and hundreds to thousands of predictors). However, the complexity of the SML models makes them opaque and hard to interpret without additional tools. There has been a lot of interest recently in developing global and local diagnostics for interpreting and explaining SML models. In this paper, we propose locally interpretable models and effects based on supervised partitioning (trees) referred to as LIME-SUP. This is in contrast with the KLIME approach that is based on clustering the predictor space. We describe LIME-SUP based on fitting trees to the fitted response (LIM-SUP-R) as well as the derivatives of the fitted response (LIME-SUP-D). We compare the results with KLIME and describe its advantages using simulation and real data.

Subfields: Artificial Intelligence

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Citations in the corpus

On the Art and Science of Machine Learning Explanations (2018, background): “• Hu et al. use local linear surrogate models, h GLM , in h tree leaf nodes to increase overall surrogate model ﬁdelity while also retaining a high degree of interpretability [16].”
Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (2021, unknown): “Hu et al. [15] proposed an approach to partition the dataset using a supervised tree-based approach.”
Explainable Neural Networks based on Additive Index Models (2018, background): “These models which may be either global (Hinton et al. (2015), Bucilua et al. (2006), Tan et al. (2018)) or local (Hu et al. (2018)) in nature.”
SurvNAM: The machine learning survival model explanation (2021, methodology): “Its success and simplicity motivated to develop several modifications, for example, ALIME [23], NormLIME [24], DLIME [25], Anchor LIME [26], LIME-SUP [27], LIME-Aleph [28], GraphLIME [29].”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “Numerous extensions of LIME exist, which try to fix issues with the original method, extend it to other tasks and data, or analyze its properties [53,93,92,121,47,94,103,12].”